ACTUAL 2026

NO PREPAYMENT!

Machine Learning and Data Science

Course and diploma theses on machine learning. TensorFlow, PyTorch, Keras, scikit-learn. Classification, regression, Computer Vision (CNN/YOLO), NLP (Transformers), clustering, Deep Learning (GAN).

from UAH 4,000 Term from 7 days

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Areas of Machine Learning

Classification

Random Forest, SVM, XGBoost, LightGBM, neural networks. Binary and multiclass classification of images, text, tabular data

from UAH 4,000

Regression and forecasting

Linear and polynomial regression, time series (ARIMA, LSTM, Prophet), price, demand, weather forecasting

from UAH 4,000

Computer Vision

CNN (ResNet, EfficientNet), YOLO v8 object detection, segmentation, face recognition, medical images

from UAH 5,000

NLP is text processing

Sentiment analysis, NER, text generation, chat bots. BERT, GPT, Transformers, word2vec, TF-IDF

from UAH 5,000

Clustering

K-means, DBSCAN, hierarchical clustering, customer analysis, market segmentation, anomalies

from UAH 3,500

Deep Learning

CNN, RNN/LSTM, Transformers, GAN, autoencoders, transfer learning, fine-tuning of pre-trained models

from UAH 6,000

A typical pipeline of an ML project

Each of our ML projects goes through a full cycle from data collection to model evaluation. Here's what it looks like:

Data collection

Kaggle, UCI Repository, Hugging Face Datasets, web scraping (BeautifulSoup, Selenium), API, social network parsing. We create or find the perfect dataset for your task.

EDA and preprocessing

Exploratory Data Analysis: distributions, correlations, outliers. Data cleaning, gap processing, category coding (OneHot, Label), normalization, feature engineering.

Model selection

Comparison of algorithms: from simple (Logistic Regression) to complex (XGBoost, Neural Networks). Cross-validation, GridSearchCV, selection of hyperparameters for optimal results.

Model training

Train/validation/test split, training with early stopping, regularization (L1, L2, Dropout), batch normalization. GPU acceleration via Google Colab or Kaggle Notebooks.

Quality assessment

Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix for classification. MSE, RMSE, MAE, R² for regression. All graphs in matplotlib and seaborn.

Documentation

Jupyter Notebooks with Markdown explanations of each step. Saved model (.h5, .pkl, .pt). README, requirements.txt, graphs and conclusions for the course/diploma.

Code example: CNN image classification

Below is a typical example of a convolutional neural network (CNN) for image classification. Such code is the basis for coursework in Computer Vision.

What this project includes:

Dataset loading and augmentation
Construction of CNN architecture
Early stopping training
Graphs of loss and accuracy
Confusion matrix and classification report
Saving and loading the model

image_classifier.py (PyTorch)

import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

class ImageClassifier(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((4, 4)),
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 4 * 4, 256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        return self.classifier(x)

How we work

You send TK

Topic, task type (classification, NLP, CV), model requirements, deadline

We evaluate

We select a dataset, discuss the architecture of the model. The assessment is free!

We execute

EDA, preprocessing, model training, quality assessment, visualization of results

Demonstration

We show the Jupyter Notebook with the results. You pay only after that!

What you get

Jupyter Notebooks with detailed explanations
Dataset and complete data preprocessing
Trained model (.h5, .pkl, .pt file)
Visualization: graphs, confusion matrix, ROC

Quality metrics: accuracy, F1, precision, recall
requirements.txt and README with instructions
Free edits to protection
Explanation of code and algorithms for protection

Reviews about ML projects

"Course on image classification - CNN on PyTorch, dataset from Kaggle, accuracy 94%. Jupyter Notebook with explanations of each layer. The teacher was impressed with the visualizations!"

Oleksiy M.

KPI, Kyiv

"Diploma in NLP sentiment analysis of reviews - BERT fine-tuning, F1-score 0.91. Complete pipeline from data collection to web interface on Streamlit. Perfectly defended!"

Sofia K.

KNU, Kyiv

"LSTM Time Series Forecasting - Stock Price Prediction. Data from Yahoo Finance, ARIMA vs LSTM vs Prophet Comparison. Detailed analysis and beautiful graphs!"

Denis P.

Khnure, Kharkiv

All reviews

Frequently asked questions about ML projects

Both frameworks are powerful and widely used.PyTorchmore popular in academia due to dynamic computational graphs, Pythonic API, and convenient debugging.TensorFlow with Kerasbest for production display and mobile devices (TF Lite). We work with both - choose depending on the requirements of your university.

Yes! We select open datasets fromKaggle, UCI ML Repository, Hugging Face Datasetsor we create our own through web scraping and data augmentation. The dataset, preprocessing and separation into train/validation/test are included in the project cost. If you have your data, we work with it.

Yes, for Deep Learning projects (CNN, RNN, Transformers) we useGPU via Google Colab Pro, Kaggle Notebooks with free GPU or custom NVIDIA GPUs. Classic ML (scikit-learn: Random Forest, SVM, XGBoost) doesn't need a GPU — everything runs efficiently on the CPU.

Depending on the task:Accuracy, Precision, Recall, F1-score, ROC-AUCfor classification;MSE, RMSE, MAE, R²for regression;Silhouette Score, Davies-Bouldin Indexfor clustering. All metrics are visualized: confusion matrix, ROC curve, learning curves, feature importance.

Yes, all the code is designed inJupyter Notebookswith Markdown explanations of each stage: data loading, EDA (Exploratory Data Analysis), preprocessing, model training, quality assessment. This is the standard for ML projects in all Ukrainian and European universities.

Yes! We work withHugging Face Transformers: BERT for text classification, GPT for generation, T5 for translation, ViT for image classification. Fine-tuning of pre-trained models, transfer learning, prompt engineering — all this is available for course and diploma theses.

Yes, we work with clients in different languages. You canwith documentation and comments in any language — Ukrainian, Russian or English.